Improved multiple target tracking using Dempster-Shafer identification

Tracking multiple maneuvering targets in clutter is a challenging problem. Using only the measured kinematic quantities is usually not adequate to meet the requirements on a multiple target tracking (MTT) system, i.e., to partition the sensor data into tracks of the targets while suppressing the clutter and false alarms. The efficient use of attribute data in addition to the kinematic measurements can greatly enhance the capability of an MTT system in discrimination against the false tracks. In this paper, the friend-foe identification information, the target run-length which measures the number of hits by a radar for a target, and the estimated target speed at each update of a track are used for true and false track identification. Since not all the information are available for all the tracks at all time, Dempster-Shafer's evidential reasoning is employed to combine these pieces of uncertain information with different levels of abstraction. Real air surveillance radar data were collected to evaluate the effectiveness of this combined tracking and identification approach. Results shows that the fusion of track attribute data with the kinematic estimates by Dempster-Shafer reasoning provides very satisfactory discrimination between the true and false tracks, thus greatly improves the system's surveillance capability over the system that uses only the kinematic data.